We are in the middle of the information age, and the digital transformation is primarily responsible for making data be treated always like the new thing for business growth. But what does it take to work the immense mass of data generated every day? Data Lake might be an option.
The recent need for companies to deal with huge amounts of information and the strategic value of analytics on all that data have given rise to new tools and concepts every day.
Among these solutions is Data Lake, a new take on information storage and processing. In this post, we’ll show you how this technology works. Stay with us and enjoy reading!
The Data Lakes in Big Data
Big Data is responsible for receiving a large body of data and generate insights after the analysis of that information. However, gathering the maximum amount of data that can actually be used in order to generate strategic knowledge is one of the main difficulties.
The solution to this problem may lie in deploying Data Lakes and its integration with Big Data. Through their large data storage capacity, lakes could provide enough information for BI systems to extract value.
Thus, we can observe that Data Lakes are not just another tool to store data in which no one knows what to do, but a strategic solution to increase the level of assertiveness in Big Data analytics.
How it works?
In Data Lake there is no need to perform any kind of pre-analysis or data structuring prior to storage. It is possible to save structured, unstructured, semi-structured, historical, logs, among others. However, it is not because there is the possibility to store everything that you should do this.
As with other storage infrastructures, the ideal is to know how to differentiate between what may have value in the future and what may be discarded, being careful not to throw away important data.
Most applications are built using open-source Hadoop technology, which further reduces costs compared to other types of data warehousing implementations.
The information chosen to be saved in the lake is sent to the system, which stores it simply and quickly. To rescue them you need to use data science techniques.
But when we are talking about data lakes then how Snowflake can be left behind?
Why Snowflake data lake?
Many big companies have split away data platforms using many data silos, manual transformations and the huge burden of data management. The Snowflake Data Cloud eliminates all those old tactics of string and using data and allows organizations to aim on their priority data tasks.
With the India Snowflake Consultants, you are able to merge data into one trustworthy resource which supports a variety of business-related applications. The risk of data loss and data leak is almost zero with Snowflake. Individuals are able to work together without the need of copying the data.
The Snowflake platform enables you to limit significant data access and reduces the responsibility of data management and the task of preventing multiple copies of the same data sets. The platform automatically eliminates complexity, to make it a smooth experience for you to use data lakes for your business growth.